Lead Machine Learning Engineer - LMTS

Salesforce•Palo Alto, CA

5d•$172,500 - $285,800•Hybrid

About The Position

Salesforce is seeking a Senior / Lead member of technical staff for Machine Learning Engineering. This role is part of the Trust Intelligence Platform organization, specifically within a foundation machine learning platform team focused on building and accelerating scalable and resilient machine learning pipelines across the security engineering organization. The ideal candidate will be a highly motivated, hands-on lead machine learning engineer with a strong business understanding of cybersecurity problems, acting as a force multiplier security data scientist for the security organization. This role involves architecting the data-driven strategy for threat detection capabilities, not just building models. The position emphasizes shaping defense strategy, detecting advanced threats, elevating the organization through mentorship and tooling, and operationalizing intelligence with production-grade models.

Requirements

3-5+ years in data science, with at least 2+ years dedicated to the cybersecurity domain.
Experience designing, implementing, and deploying systems of anomaly detection, clustering, and graph models in production.
Hands-on comfort with high-volume logs.
Proficiency with Spark/Pyspark, Snowflake, Flink, and streaming services such as Apache Kafka.
Deep understanding and application of containerization (Docker) and workflow orchestration (Kubernetes, Apache Airflow) for automated ML pipelines.
Mastery of Python programming, including proficiency in leading ML frameworks (TensorFlow, PyTorch).
Adherence to software engineering best practices.
Demonstrated success in implementing comprehensive MLOps methodologies (CI/CD pipelines, testing protocols, model performance monitoring).
Solid foundation in feature engineering techniques and the implementation of feature stores.
Experience in formulating ML governance policies and ensuring adherence to data security regulations.
Ability to explain complex statistical concepts to non-technical stakeholders and executive leadership.
Proven ability to manage scope, timelines, and stakeholder expectations across multiple organizations.
High degree of autonomy with the ability to look at a vague business problem and structure a data-driven solution without needing a predefined roadmap.

Nice To Haves

Masters or PhD in a quantitative field.
Expertise in advanced Natural Language Processing (NLP) methodologies.
Experience contributing to open-source security data science tools.
Presentations at major security conferences (Black Hat, DEF CON, BSides) or data conferences.
Background in offensive security (Penetration Testing/Red Teaming) with an "attacker's mindset."
Demonstrated experience conducting research or working collaboratively with Machine Learning (ML) research teams.
Previous experience in a mentoring role for junior engineers.
Track record of publications and/or patents in quantitative disciplines.

Responsibilities

Own the decision-making process for translating security threats into mathematical problems.
Champion a rapid prototyping culture to validate hypotheses quickly.
Lead the evolution of threat detection using advanced probabilistic modeling, graph analytics, and supervised/unsupervised learning.
Expose sophisticated threats like active system intrusions, lateral movement, beaconing, and insider attacks.
Act as a force multiplier by mentoring junior scientists and engineers.
Build internal tooling, feature stores, and libraries to improve team efficiency.
Influence the broader security engineering roadmap to ensure closed-loop security telemetry.
Deliver production-grade models with engineering rigor (CI/CD, scalable code) and adversarial resilience.
Minimize alert fatigue and maximize analyst efficiency.